A promising alternative to LML or CML is presented by Federated Learning (FL). Instead of exchanging data, this ML framework relies on training many local models of identical structure at the location of the data, which are then combined into a global model. In our case, the data are stored at different hospitals, which we refer to as clients. Apart from ensuring privacy by design, FL enables parallel training using the clients as computational units [1]}, [2]}.
